High-throughput intensity diffraction tomography with a computational microscope

Abstract

We demonstrate a motion-free intensity diffraction tomography technique that enables direct inversion of 3D phase and absorption from intensity-only measurements for weakly scattering samples. We derive a novel linear forward model, featuring slice-wise phase and absorption transfer functions using angled illumination. This new framework facilitates flexible and efficient data acquisition, enabling arbitrary sampling of the illumination angles. The reconstruction algorithm performs 3D synthetic aperture using a robust, computation and memory efficient slice-wise deconvolution to achieve resolution up to the incoherent limit. We demonstrate our technique with thick biological samples having both sparse 3D structures and dense cell clusters. We further investigate the limitation of our technique when imaging strongly scattering samples. Imaging performance and the influence of multiple scattering is evaluated using a 3D sample consisting of stacked phase and absorption resolution targets. This computational microscopy system is directly built on a standard commercial microscope with a simple LED array source add-on, and promises broad applications by leveraging the ubiquitous microscopy platforms with minimal hardware modifications.

References in corpus (4)

• Wide-field, high-resolution Fourier ptychographic microscopy
• Computational illumination for high-speed in vitro Fourier ptychographic microscopy
• Active illumination using a digital micromirror device for quantitative phase imaging
• Tomographic active optical trapping of arbitrarily shaped objects by exploiting 3-D refractive index maps

Cited by in corpus (19)

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• Reliable deep-learning-based phase imaging with uncertainty quantification
• High-speed in vitro intensity diffraction tomography
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• SIMBA: Scalable Inversion in Optical Tomography using Deep Denoising Priors
• Bond-Selective Intensity Diffraction Tomography
• High-fidelity intensity diffraction tomography with a non-paraxial multiple-scattering model
• SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
• Holographic particle localization under multiple scattering
• Tensorial tomographic Fourier Ptychography with applications to muscle tissue imaging
• Quantitative phase and refractive index imaging of 3D objects via optical transfer function reshaping
• Single-shot refractive index slice imaging using spectrally multiplexed optical transfer function reshaping
• Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples
• Scalable Plug-and-Play ADMM with Convergence Guarantees
• Uniqueness Theorems for Tomographic Phase Retrieval with Few Diffraction Patterns
• Inverse scattering for reflection intensity phase microscopy
• 3D Tomographic Phase Retrieval and Unwrapping
• Physics-based Learned Design: Optimized Coded-Illumination for Quantitative Phase Imaging
• Deep Learned Optical Multiplexing for Multi-Focal Plane Microscopy